Improvement on the Viscosity Models for the Effects of Temperature and Pressure on the Viscosity of Heavy Crude Oils
Bibliographic record
Abstract
With the industrial need for oil creating a growing scarcity of light, transport-efficient oil, a need for more readily transportable heavy crude is being developed. The processes of production and pipeline transportation of heavy crude oil require an understanding of material composition and physical and rheological characteristics, and the changes which occur at different process parameters. Typically this would require a series of expensive and wasteful experiments, done by exposing various crudes to varied processing conditions, such as pressure and temperature, which through exposure produce changes in the oil properties but are overall inefficient. Modeling eliminates these needs as a simple equation can predict heavy crude oil characteristics given parameters of use. This study comparatively examines a new simplistic, semiempirical equation against current empirical models for the viscosity of Tangleflags and Athabasca bitumen. The equation gave values of relatively low percent errors for the effect of temperature and pressure on viscosity based on one viscosity measurement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".